Effects of different treatments for type 2 diabetes mellitus on mortality of coronavirus disease from 2019 to 2021 in China: a multi-institutional retrospective study

The coronavirus disease (COVID-19) pandemic has continued for 5 years. Sporadic cases continue to occur in different locations. Type 2 diabetes mellitus (T2DM) is associated with a high risk of a poor prognosis in patients with COVID-19. Successful control of blood glucose levels can effectively decrease the risks of severe infections and mortality. However, the effects of different treatments were reported differently and even adversely. This retrospective study included 4,922 patients who have been diagnosed as COVID-19 and T2DM from 138 Hubei hospitals. The clinical characteristics and outcomes were compared and calculated their risk for death using multivariate Cox regression and Kaplan–Meier curves. After adjustment of age, sex, comorbidities, and in-hospital medications, metformin and alpha-glucosidase inhibitor (AGI) use performed lower all-cause mortality (adjusted hazard ratio [HR], 0.41; 95% confidence interval [CI]: 0.24–0.71; p = 0.001 for metformin; 0.53, 0.35–0.80, p = 0.002 for AGIs), while insulin use was associated with increased all-cause mortality (adjusted HR, 2.07, 95% CI, 1.61–2.67, p < 0.001). After propensity score-matched (PSM) analysis, adjusted HRs for insulin, metformin, and AGIs associated with all-cause mortality were 1.32 (95% CI, 1.03–1.81; p = 0.012), 0.48 (95% CI, 0.23–0.83, p = 0.014), and 0.59 (95% CI, 0.35–0.98, p = 0.05). Therefore, metformin and AGIs might be more suitable for patients with COVID-19 and T2DM while insulin might be used with caution. Supplementary Information The online version contains supplementary material available at 10.1186/s43556-024-00183-1.


Introduction
The coronavirus disease (COVID-19) pandemic has continued for 5 years since the first case was reported in December 2019.In the early stage, the novel coronavirus was well-known for its highly contagious and severe respiratory symptoms which bring a terrible blow to public health around the world.As time went on, the proper management has been conducted and vaccines for the virus have been developed which seems to mean that the pandemic was going to the end.It was unexpected that the virus was mutating at a very rapid rate.As of Aug 9, 2023, there have been over 700 million cases and over than six million deaths globally reported by the World Health Organization.Sporadic cases still exist in different locations, although most countries have implemented COVID-19 control.
Type 2 diabetes mellitus (T2DM) is associated with a high risk of a poor prognosis in patients with COVID-19, and patients with COVID-19 and diabetes might develop acute respiratory distress syndrome (ARDS) and severe viral infection [1][2][3][4].Successful control of blood glucose can effectively decrease the risk of severe infection and mortality [2,5].Therefore, glucose control in patients with COVID-19 and T2DM is crucial to reduce the risk of complications and poor prognosis.Post-COVID-19 T2DM in the context of long COVID-19 has been paid more attention to, and how to manage these patients has become an important topic [6,7].
In the early stages, some studies suggested that insulin treatment was prioritized over oral hypoglycemic drugs in treating patients with COVID-19 and T2DM [8,9].However, at that time, evidence was lacking.An association between metformin treatment and a lowered risk was proposed in many studies [10][11][12][13].Our laboratory study found that insulin treatment in patients with T2DM significantly increased mortality and promoted infection and inflammation in different organs, contrary to common reports [14].
To determine the effect of different antidiabetic drugs on the outcomes of patients with COVID-19 and T2DM, we used the data of patients with COVID-19 from December 2019 to August 2021 in Hubei Province, China, to compare common antidiabetic drugs, such as insulin, metformin, alpha-glycosidase inhibitors, sulfonylureas, glinides, and DPP4 inhibitors.The results might provide more suggestions and evidence regarding which drugs might be a priority to manage patients with COVID-19 and T2DM or post-COVID-19 T2DM.

Participants
Before August 31, 2021, 68,128 subjects confirmed with COVID-19 participated in our study from 138 hospitals in Hubei Province.Of these, missing data helped us exclude 15,098 patients (22.2%).In the end, the number of 53,030 patients were included in the study.Among them, 4,922 (9.28%) were diagnosed with T2DM.The median age of the 4,922 patients was 66 (58-76) years, and 53.4% of the patients were men (Table 1).Table 1 shows the other characteristics of the T2DM and non-T2DM groups and the flow chart is shown in Fig. 1.Compared to the non-T2DM group, the T2DM group had higher proportions of cardiovascular diseases, chronic obstructive pulmonary disease and chronic kidney disease.

Primary outcomes
In sight of mortality, 387 of 4,922 patients in the T2DM group performed better than 3,323 of 48,108 patients in the non-T2DM group (7.9% vs. 6.9%,p = 0.013).The severity ratio in the T2DM group performed higher than the non-T2DM group (23.4 vs. 32.2,p < 0.001).
We used Kaplan-Meier curves to compare survival time and status.The mortality risk was significantly higher in the T2DM group than in the non-T2DM group (Fig. S1).
Considering the existing imbalanced confounding factors, we conducted PSM.We matched 673 patients who were treated with insulin with who were treated without insulin, 405 patients who were treated with metformin with who were treated without metformin, and 496 patients who were treated with AGIs with who were treated without AGIs in a 1:1 ratio.In all, 1,346 patients were included in the PSM analysis.The balance was evaluated with SMD and p-values (Tables S1-S3).For the result of PSM, multivariate Cox regression performed consistent results with Kaplan-Meier curves which was also similar to the above (Fig. 3d-f ).The adjusted HRs were 1.32 (95% CI, 1.03-1.81,p = 0.012) for insulin, 0.48 (95% CI, 0.23-0.83;p = 0.014) for metformin, and 0.59 (95% CI, 0.35-0.98;p = 0.050) for AGIs.PSM has also been applied to other drugs, such as sulfonylureas, glinides, and DPP4 inhibitors, at a ratio of 1:1.Tables S4-S6 shows their balance.Th0e multivariate Cox regression model and Kaplan-Meier curves revealed no statistically significant differences, similar to those before PSM (Fig. S2d-f ).

Routine blood test
Red blood cell count, ×10 According to the aforementioned results, we hypothesized the presence of a strong relation between insulin, metformin, and AGIs uses and the higher risk.

Secondary outcomes
ARDS, invasive mechanical ventilation, acute kidney injury, and extracorporeal membrane oxygenation (ECMO) were chosen as secondary outcomes.Acute kidney injury appeared less than in the insulin group than in the non-insulin group (10.2% vs. 7.8%, p = 0.033), and the patients using invasive mechanical ventilation and ECMO were more in the insulin group than in the noninsulin group (invasive mechanical ventilation, 10.6% vs. 3.0%, p < 0.001; ECMO, 1.2% vs. 0.3%, p = 0.001; Table S1).However, the incidences of ARDS and acute kidney injury were significantly lower in the metformin group than in the non-metformin group (ARDS, 0.6% vs. 3.0%, p = 0.006; acute kidney injury, 3.2% vs. 8.8%, p < 0.001; Table S2).ARDS also occurred less in the AGI group (1.0% vs. 3.0%; p = 0.007; Table S3).Multivariate Cox regression analysis performed the adjusted HRs 1.25 (95% CI, 0.96-1.63;p = 0.100) for acute kidney injury, 4.12 (95% CI, 3.03-5.59,p < 0.001) for invasive mechanical ventilation, and 2.39 (95% CI, 0.89-6.42,p = 0.084) for ECMO.The adjusted HR for metformin was 0.28 (95% CI, 0.08-0.96,p = 0.043) for ARDS and 0.45 (95% CI, 0.26-0.77,p = 0.004) for acute kidney injury and the adjusted HR for AGIs associated with the risk of ARDS was 0.38 (95% CI, 0.15-0.94,p = 0.037; Table S7).Table S8 shows the adjusted HR for other drugs in the secondary outcomes.The results of the analysis of secondary outcomes suggested that the use of insulin was associated with a poor prognosis and that the use of metformin or AGIs was associated with a good prognosis.

Comparison between insulin treatment and other antidiabetic drugs
The multivariate Cox regression model, Kaplan-Meier curves, and PSM revealed that insulin treatment was associated with a higher risk of all-cause mortality, while  metformin and AGI treatments were associated with a lower risk.This contradicts our findings.To ensure accuracy and improve credibility, we selected patients taking the three drugs to compare the advantages and disadvantages of the combination of different drugs.Because of the opposing influence of insulin treatment, we first compared insulin and metformin treatments.The mortality of patients who received insulin alone was significantly higher than that of patients who received metformin alone (13.3% vs. 2.3%, p < 0.001).Kaplan-Meier curves showed that patients treated with metformin alone showed a significantly lower risk of all-cause mortality than those who received combined medication or insulin alone (Fig. 4a).The adjusted HR for individual insulin treatment was 1.26 (95%CI, 1.12-1.42,p < 0.001).Similar results were obtained in the comparison between insulin and AGI treatments.The mortality rates of patients who received the insulin or AGI treatment alone were 14.6% and 3.8%, respectively (p < 0.001).The adjusted HR for individual insulin treatment was 1.37 (95% CI, 1.21-1.55,p < 0.001; Fig. 4b).Insulin treatment is associated with a poor prognosis in patients with COVID-19 and diabetes.

Subgroup analysis
To observe the influence of antidiabetic drugs on mortality in different inhospital conditions and reduce the error from different laboratory results, we chose insulin and metformin use and compared their effects on all-cause mortality in different situations.First, we used Kaplan-Meier curves and multivariate Cox regression analyses in critically ill patients.Insulin treatment showed a higher risk of mortality (adjusted HR, 1.85, 95% CI, 1.35-2.55,p < 0.001), and metformin treatment showed a lower risk (adjusted HR, 0.51, 95% CI, 0.27-0.99,p = 0.046; Fig. 5a  and b).Therefore, we hypothesized that different blood glucose levels might also have an influence.We chose fasting blood glucose and glycated hemoglobin (HbA1c) levels to compare treatments.The statistical analysis showed that in patients with poor glucose control and blood glucose levels > 10 mmol/L, insulin treatment was also associated with a higher risk, and metformin treatment was associated with a lower risk.The adjusted HRs were 0.25 (95% CI, 0.08-0.82,p = 0.022) for metformin treatment and 1.43 (95% CI, 0.94-2.16,p = 0.095) for insulin treatment (Fig. 5c and d).In the HbA1c > 6.5% group the adjusted HR was 0.49 (95%CI, 0.25-0.97,p = 0.042) for metformin treatment and 1.57 (95% CI,1.12-2.19,p = 0.009) for insulin treatment (Fig. 5e-f ).Finally, we analyzed them in patients with heart injury whose NT-proBNP level exceeds 265 ng/µL.We obtained similar results.The adjusted HR was 0.28 (95% CI, 0.11-0.68,p = 0.005) for metformin treatment and 1.28 (95% CI, 0.90-1.82,p = 0.168) for insulin treatment (Fig. 5g  and h).Fig. S3a-f shows patients with a blood glucose level ≤ 10 mmol/L, HbA1c ≤ 6.5%, or NT-proBNP < 265 ng/µL, with the same results as shown above.
In short, patients classified into different groups showed similar outcomes.Insulin treatment was always associated with decreased survival, while metformin treatment was associated with increased survival.

Dynamic profiles
To evaluate the characteristics and dynamic changes after treatment, we monitored the vital signs and laboratory results, including blood glucose, systolic blood pressure, lymphocyte count, NT-proBNP level, D-dimer level, and C-reactive protein (CRP) level.Blood glucose levels in all three groups became lower after admission and tended to stabilize (Fig. 6a).Contrary to the metformin and AGI groups, the insulin group showed lower systolic blood pressure and lymphocyte counts and higher NT-proBNP and D-dimer levels (Fig. 6b-e).This suggests the possibility of an association among insulin treatment, heart injury, and coagulation disorders.The CRP level reached its highest on day 4 in the insulin group and became gradually lower but still remained higher than that in the other groups, indicating that patients in the insulin group might develop more severe infection and inflammation than those in the other groups (Fig. 6f ).These results suggested that insulin treatment may worsen organ injury during infection, leading to increased mortality.

Discussion
In the present retrospective study, insulin and other antidiabetic drug were investigated and significant statistical differences in all-cause mortality in patients treated with insulin, metformin, and AGIs were shown.Treatments with metformin and AGIs were associated with a lower mortality risk, while insulin treatment might bring about increased mortality.A similar result was observed in the subgroup analysis, classified according to baseline characteristics.These results suggest that insulin treatment came to worse outcomes, whereas metformin and AGIs were associated with better outcomes.
Evidence suggests that preexisting T2DM is significantly associated with the morbidity and mortality of COVID-19 [15][16][17].Approximately 20% of patients with COVID-19 develop T2DM, and 10% of patients are diagnosed with T2DM [18,19].Compared to patients without T2DM, those with T2DM may have a higher ratio of critical illness, mortality, and severe complications, such as respiratory failure and heart failure [17,[20][21][22].Poor disease control in patients with T2DM may promote severe infections, including those by SARS-CoV-1, H1N1, and Middle East respiratory syndrome coronavirus [23,24].High blood glucose levels may increase angiotensin-converting enzyme 2 levels in lung tissue, which mediates the SARS-CoV-2 infection into cells and aggravate infection [25,26].Meanwhile, a study found that severe infection could also worsen the hyperglycemic state, creating a vicious circle [2,27].Therefore, the management of T2DM in patients with COVID-19 is worthy of attention.No consensus exists regarding which antidiabetic drug should be prioritized for treating patients with COVID-19 and T2DM.Some observational and retrospective studies suggested that metformin treatment might exert a favorable effect, while others suggested that metformin treatment might result in poor outcomes, such as acidosis and severe respiratory difficulty [10,28].In some in vitro studies, metformin acted against SARS-CoV-2 with antiinflammation actions, including reducing interleukin-1β and interleukin-6 levels and inflammasome activation [29][30][31][32].A randomized controlled trial published in the NEJM provided evidence that metformin did not prevent the occurrence of hypoxemia, hospitalization, or death from COVID-19 [33].AGIs might disrupt SARS-CoV-2 replication, similar to SARS-CoV-1 [34][35][36].In an in vitro study by Miglustat and Celgosivir, two AGIs showed antiviral potential against SARS-CoV-2, particularly against severe respiratory infection [37].A clinical study on the positive effect of AGIs showed that AGI treatment performed lower mortality [38].
The role of insulin in patients with COVID-19 and T2DM remains controversial.In early stages, most statements and guidelines recommended insulin injections to control hyperglycemia [15,39].However, our previous laboratory study found that insulin treatment might significantly worsen viral infections by elevating interleukin-6 levels and the ratio of severe complications to death [14].Randomized controlled trials of insulin remain lacking, and more evidence is required for clinical practice.
In the present study, metformin and AGI treatments were associated with a lower risk of mortality, whereas the insulin treatment was associated with increased mortality.Clinical practitioners should be cautious when administering insulin to patients with COVID-19; metformin or AGIs may be better choices.
This study still has several limitations.First, the data were collected from December 2019 to August 2021, which may have lagged behind the current pandemic situation.Due to the urgent state at that time, comprehensive monitoring of various indices was lacking in inpatients.Second, this was only retrospective that could not describe causal effects and only provided clinical evidence for therapy.Therefore, more randomized clinical trials are required in the future.

Conclusion
Insulin was associated with a higher risk of all-cause mortality in patients with COVID-19 with T2DM, while metformin and AGIs were associated with a lower risk.In patients with COVID-19 and T2DM, metformin or AGIs might be prioritized over insulin.

Study design
This study included patients with COVID-19 from 138 hospitals in the Hubei Province.They were confirmed COVID-19 on the basis of the China and World Health Organization guidelines, and those who died or were discharged between December 29, 2019 (i.e., when the first patients were admitted) and August 31, 2021, were included in the present study.Those aged < 18 years and lacking important basic information, laboratory test results for SARS-CoV-2 were excluded.The electronic medical record database provided all data from the Health Commission of Hubei Province.
To deal with center effects, firstly, we have excluded any hospitals with less than 100 cases.Then, in the implementation phase, we used the same electronic management system to collect data quickly and correctly and the same diagnosis and treatment criteria for COVID-19 which was published by Hubei Provincial Health Commission.Meantime all medical workers have been professionally trained to collect and process data in the uniform standard.
Finally, 53,030 inpatients with COVID-19 were enrolled in this study.Of them, 4,922 patients suffered from T2DM diagnosed according to the guidelines published by the Chinese Diabetes Society and were managed according the recommendation of the Chinese Diabetes Society [40].

Data collection and endpoint definitions
The examination of data from electronic medical records were conducted individually by three researchers.The information included age, sex, comorbidities, vital signs, laboratory tests, therapeutic strategy, and outcomes.
Primary endpoint was defined as all-cause death and second outcomes were defined as life-threatening acute injury and need for life-support treatment such as ARDS, invasive mechanical ventilation, acute kidney injury and extracorporeal membrane oxygenation (ECMO).

Propensity score-matching (PSM) and Cox regression analysis
To balance the differences in covariates between groups, PSM was performed.We adjusted for confounding factors including sex; age; severity; laboratory results, such as white blood cell count, glutamic-pyruvic transaminase level, creatinine level, N-terminal pro-B-type natriuretic peptide (NT-proBNP) level and other associated diseases, such as chronic kidney diseases and heart failure.These indicators were selected referred to published risk factors and these included in other PSM studies [3,4,14,28].Statistical analyses were performed by R MatchIt Package.The caliper width was set as 0.05.The standardized mean difference (SMD) was used to evaluate the balance of covariates after matching and p-value < 0.1 indicated successful matching.
Multivariate Cox regression models were used to further adjust imbalanced covariates.Results are expressed as hazard ratio (HR) and 95% confidence interval (CI).Survival analysis methods were used to compare the timing of endpoint events among subgroups.

Statistical analysis
Statistical analyses were performed using SPSS (version 24.0; IBM, Armonk, NY, USA) and R (version 4.1.1;R Foundation for Statistics Computing, Vienna, Austria).D' Agostino's and Pearson's omnibus normality tests were used to assess the distribution and homoscedasticity of each dataset.Continuous normally distributed data were expressed as mean (standard deviation) and compared using the Student's t-test.Median (interquartile range) were used to express continuous non-normally distributed data.They were compared using the Wilcoxon ranksum test.Categorical data are expressed as n (percentage) and compared by the chi-square, Fisher's exact, and Cochran-Mantel-Haenszel tests, as appropriate.

Patient and public involvement
In our study it was not appropriate or possible to involve patients or the public in the design, or conduct, or reporting, or dissemination plans of our research because any their identifiable information such as ID and telephone were hid by the Hubei Provincial Health Commission and patients may be lack of related professional knowledge.

Fig. 1 Fig. 2 Fig. 3 Fig. 4
Fig.1The flowchart about the process of participant enrolment and data analysis.a 4922 participants with a history of T2DM were classified into the disease group; b, c, d 817, 466 and 608 patients with T2DM who were taking insulin, metformin and AGIs during hospitalization were enrolled in the insulin group; e, f, d 673, 405 and 496 patients with T2DM who were taking insulin, metformin and AGIs during hospitalization were enrolled in the insulin group after PSM

Fig. 5 Fig. 5 (
Fig. 5 Subgroup analysis of different clinical conditions in patients with metformin or insulin treatment.a, b The survival curves of in-hospital mortality for critically ill patients with metformin or insulin treatment were shown; c, d The survival curves of in-hospital mortality for patients under poorly controlled glucose (glucose > 10 mmol/L) with metformin or insulin treatment were shown; e, f The survival curves of in-hospital mortality for patients under poorly controlled HbA1c (HbA1c > 6.5%) with metformin or insulin treatment were shown; g, h The survival curves of in-hospital mortality for patients under high NT-proBNP on admission (NT-proBNP > 265 mmol/L) with metformin or insulin treatment were shown (See figure on next page.)

Fig. 6
Fig. 6 Dynamic Profile of Vital Signs and Laboratory Parameters in All Patients with COVID-19 and T2D with insulin, metformin and AGIs treatment.a, b, c, d, e, f The line charts showed dynamic change of blood glucose (mmol/L), systolic blood pressure (mmhg), lymphocyte count (10^9/L), NT-proBNP (pg/ml), D-dimer (mg/ml) and C-reaction protein (pg/ml) with days after admission

Table 1
Characteristics of patients with COVID-19 in the type 2 diabetes and non-T2D